title: A model to predict antiviral treatment in HBeAg negative chronic hepatitis B with alanine aminotransferase≤2 upper limit of normal.
abstract: OBJECTIVE
  Liver histological assessment is essential for predicting antiviral therapy in HBeAg negative chronic hepatitis B (CHB) patients with serum alanine aminotransferase (ALT)≤2 upper limit of normal (ULN). The aim was to establish a model to predict antiviral treatment for those patients without liver biopsy.
  
  
  METHODS
  Three hundred and one consecutive treatment naive HBeAg negative CHB patients with HBV DNA≥2000 IU/ml and ALT≤2 ULN were retrospectively enrolled, among which 158 patients were for the training set and 143 for validation set. A multivariate logistic regression model was constructed in the training set and validated in the validation set.
  
  
  RESULTS
  Our model identified four independent factors for the timing of treatment: Age (OR 1.050, 95%CI 1.004-1.098), Ln(aspartate aminotransferase) (OR 17.425, 95%CI 5.394-56.292), Log10 [HBV DNA] (OR 0.704, 95%CI 0.514-0.963) and platelet (OR 0.980, 95%CI 0.970-0.990). It showed 94% sensitivity, 88% negative predictive value (NPV) in the training set and 93% sensitivity, 85% NPV in the validation set using the low cut-off point of 5.16. Meanwhile, it showed 92% specificity, 88% positive predictive value (PPV) in the training set and 94% specificity, 92% PPV in the validation set using the high cut-off point of 7.26. It could predict treatment for 179 of 301(59%) patients without biopsy.
  
  
  CONCLUSIONS
  We established a model to predict antiviral therapy in HBeAg negative CHB patients with ALT≤2 ULN. Antiviral treatment should be initiated if the model value>7.26 and not if its value≤5.16. Liver biopsy is needed only when its value between the two points.
authors.affiliation: Department of Hepatology, Qilu Hospital of Shandong University, Jinan, China.
authors.forename: Shuai
authors.initials: S
authors.lastname: Gao
authors.forename: Yu-Chen
authors.initials: YC
authors.lastname: Fan
authors.forename: Jing
authors.initials: J
authors.lastname: Zhao
authors.forename: Feng-Kai
authors.initials: FK
authors.lastname: Sun
authors.forename: Jie
authors.initials: J
authors.lastname: Han
authors.forename: Ze-Hua
authors.initials: ZH
authors.lastname: Zhao
authors.forename: Kai
authors.initials: K
authors.lastname: Wang
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000410:Alanine Transaminase / Q000097:blood*; D000998:Antiviral Agents / Q000627:therapeutic use*; D001219:Aspartate Aminotransferases / Q000097:blood; D001792:Blood Platelets / Q000378:metabolism; D004279:DNA, Viral / Q000097:blood; D003661:Decision Support Techniques; D005260:Female; D006513:Hepatitis B e Antigens / Q000097:blood; D019694:Hepatitis B, Chronic / Q000097:blood / Q000188:drug therapy* / Q000473:pathology; D006801:Humans; D008099:Liver / Q000473:pathology*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D016017:Odds Ratio; D012189:Retrospective Studies
chemical_list: D000998:Antiviral Agents; D004279:DNA, Viral; D006513:Hepatitis B e Antigens; D001219:Aspartate Aminotransferases; D000410:Alanine Transaminase
keywords: antiviral therapy; chronic hepatitis B; liver biopsy; non-invasive model